Over the past, several measures have been taken to detect impairments of one or more stereo cameras of a stereo vision system. The forms of impairments can include camera obstruction of a partial or complete field of view blockage either by solid objects such as leaves on a windshield, environmental factors such as precipitation (water, snow or fog) or low-light conditions, problems with the optical system itself such as poor camera focus, poor camera calibration, poor stereo camera relative alignment or other unanticipated problems with the video. Additionally, low target contrast or texture (while not a camera impairment per se) can also cause poor system measurements when viewing the video images. For example one of these impairments could cause a critical error in stereo measurements by altering the relative orientation of the left and right camera, without benefit of a compensatory recalibration, which in turn would cause incorrect resulting depth computations, etc.
Collision detection systems are known in the art to compute stereo images to detect potential threats in order to avoid collision or to mitigate its damage. The impairments could easily cause the collision algorithms to misidentify these incorrect measurements as potential collision threats, thus creating a false alarm, the effects of which could be drastic. Thus the presence of such impairments, once identified, should cause the system to temporarily disable itself for the duration of the impairment, sometimes called a “failsafe” condition. This would be applicable also in less severe applications, which provide for much wider range of safety and convenience functions, for example, adaptive cruise control.
Stereo depth estimate accuracy can be computed precisely for a given stereo algorithm on a stereo image data with known position and/or ground-truth information. However, this ground-truth information may be unavailable or difficult to collect for real-world scenes, even in controlled settings, and are certainly not available in the uncontrolled settings of a deployed stereo or monocular imaging system. Moreover, such characterizations only measure the accuracy of a stereo algorithm under ideal conditions, and ignore the effects of the kinds of unanticipated impairments noted above. That is, a characterization of a stereo algorithm's accuracy under ideal conditions does not predict and is not able to measure its robustness to various impairments found in uncontrolled real-world conditions.
Some algorithms may attempt to characterize specific impairments such as rain or fog in an operating imaging system using specific characteristics of the impairment itself (such as expected particle size and density), but may not generalize to other impairments such as hail, sleet or a sandstorm and therefore would not be able to reliably invoke a needed failsafe condition. Thus the deployment of practical imaging systems, particularly stereo imaging systems, has a need for a general means to measure both monocular and stereo image quality.